code: def __init__(self, dataset, num_hops, split="train"):
      self.data = dataset[0]
      self.num_hops = num_hops
      super().__init__(dataset.root)
      index = ["train", "val", "test"].index(split)
      self.load(self.processed_paths[index])
  
imports: import math
  import os.path
  import time
  from itertools import chain
  import numpy
  import torch
  import torch.nn.functional
  from scipy.sparse.csgraph import shortest_path
  from sklearn.metrics import roc_auc_score
  from torch.nn import BCEWithLogitsLoss, Conv1d, MaxPool1d, ModuleList
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.datasets import Planetoid
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, GCNConv, SortAggregation
  from torch_geometric.transforms import RandomLinkSplit
  from torch_geometric.utils import k_hop_subgraph, to_scipy_sparse_matrix
label_desc: Use image node_id 1 to create a new SEALDataset object from inherited base classes: InMemoryDataset with example: obj = SEALDataset(dataset, num_hops, split)